Quantitative research approach
Professor Yvonne Brunetto,
Professor Stephen Teo,
Professor Jarrod Haar
Three basic criteria determines
which method to use
1. The type of research question. E.g
“Why” and
“How” questions are ideally suited to a case study approach.
2. The extent to which the researcher has control
over the subject and context of the subject e.g.,
The experimental approach is more suitable for controlled
environments, whereas the case study approach is better if
you can’t control subjects or the context
3. The period of history under examination
influences the choice of methodology. E.G if
you want to study an historical phenomena ,then you have to
use archives information
The Need for a Framework
The conceptual/theoretical
framework is a logically
developed, described and
elaborated network of
associations among concepts or
variables deemed relevant to the
problem situation.
The basis of quantitative research:
Central Tendency
Across any one SAMPLE there will typically be a standard
distribution of a particular property, for example it is said
that intelligence across a country’s population will adhere to a
standard distribution.
F
r
e
q
u
e
n
c
y
Populations and samples that adhere to a standard distribution
will have a majority of participations existing in the centre of
any distribution – and this frequency will decrease as the
deviations get closer to the extremes at any end of the
distribution
Theoretical Framework
1. Types of variables
•
•
•
•
Dependent
Independent
Moderating
Intervening
2. Components of Theoretical
Framework
Concepts and Variables
A Concept
• an idea expressed as a symbol or in
words
• Conceptual framework
A Variable
• Can be observed and measured
• Theoretical framework
Dependent variable
• A dependent variable is a measurable outcome
of an experiment.
– For example communication satisfaction,
productivity, number of sick days, employee morale,
or organisational commitment could be dependent
variables. There is a clear advantage if the
dependent variable is easily measured; sick days are
much easier to measure than employee morale.
Independent variable(s)
• An independent variable or treatment variable
represents a quality or characteristic that is
varied or manipulated during the experiment.
– examples include quality of feedback, training
methods, remuneration, or work hours. The
independent or treatment variable is manipulated to
determine the effect on the dependent variable. This
process is the treatment received by the participants.
The group receiving the treatment is referred to as
the treatment or experimental group.
Dr Apivut Chakuthip
thesis
TAM Beliefs
Attitude**
Trust in Social
Network
Perceived
Behavioral
Control**
Subjective Norm**
Behavioral
Intention
EC Adoption**
Theoretical
Framework
Independent variables
•POS (Perceived
organisational
support)
Social
Exchange
Theory
•Procedural justice
•Interactional
justice
•LMX
•Trust
•Organisational
culture
•Tie Strength
Dependent Variables
Intention to
turnover of nursing
professionals
Affective
Commitment
OCB
Innovative
Behaviour
Dr Matt Xerri ‘s Theoretical framework
Independent
Variables
Training and
Development
Communication
Processes:
•Frequency
•Informal
•Indirect
•Two-way
Moderator
Ambiguity
Regarding
Customers
Dependent
Variables
Job
Satisfaction
Employee
Performance
An
example of
a
conceptual
framework
Affective
Commitment
Professionalism
Dimensions:
•Referent
•Self-Regulation
•Autonomy
Organisation
Occupation
Customers
By Dr
Natasha
Currant
Using SEM for your research
Professor Stephen Teo
Management Department
AUT Business School
What is SEM [structural equations modelling]?
 A technique for testing theoretical models
 Researcher specifies their model and how the various
constructs should influence each other
 Hoyle’s (1994) review tells us that SEM can address:
 Questions about causal process
 Basic questions of measurement
 Questions about causal process when variables are not well
measured
 SEM methods share most of the strengths of OLS
multiple regression
 SEM tests this model statistically
 Incorporates the features of factor analysis and regression
analysis
Strengths
 Proposed causal explanations are made explicit
 Tests of fit allow implausible models to be rejected
 Competing models can often be compared, and one
may emerge as more plausible given the data.
Limitations
 Models are often mis-specified (needs theorizing):
 Linearity assumption is often made uncritically
 Measurement error distorts analysis
 Important variables may be missing
 Communicating results is challenging
 Novices may overstate claims or make errors in
complex analyses that are difficult to detect
Covariance-based SEM
 Question to ask during the modelling process
 Could this model have led to the data that I have?
 That’s why in AMOS, Mplus and even PLS, we
refer to goodness of fit indices to show model fit
(eg. χ2/df=2.076, CFI=.95, TLI=.94, RMSEA=.04,
SRMR=.07
Model
Data
Figure 2a. Results of Analysis using Mplus (Female)
WLB
.09**
Info
Provision
(.12*)
AC
R2=41.3%
(.06*)
.23***
Involvement
.55***
.31***
Job
Satisfaction
.29***
Influence
Goodness of fit: 2 /df=1.77,
RMSEA=0.032, CFI=0.991,
TLI=.988, SRMR=0.021
Source: Ravenswood and Teo (2014) in AIRAANZ Conference
• As Yvonne mentioned, “Theory” is important
and SEM, is a theory driven process
– Theory is specified as a model
• Alternative theories can be tested
– Specified as models (Q: simple or complex?)
Theory B
Theory A
Data
Ambidex
Strg Mgt
Org Sys
Org
Perf
HRM
System
Work
Attitudes
Strg HRM
Source: Plimmer, Teo and Bryson (2014): using PLS
Partial Least Squares (PLS) Modelling
 PLS is a latent path model, a well-established technique
for estimating path coefficients in causal modelling
 Statistical basis initially formed in the late 60s
through the 70s by econometricians in Europe
The conceptual core of PLS is an iterative combination
of principal components analysis relating measures (each
questionnaire item) to constructs (latent variables or
factors), and path analysis permitting the construction of
a system of constructs
Allows for the simultaneous testing of hypotheses, unlike
multiple regression, within the same statistical analysis
Structural (path) model (lines and
circles)
2nd
order
LV
1st order
LV
Measurement (items within each
construct) model (blue)
Positives of PLS Modelling
Does not require normal data
Accept smaller sample sizes because “each causal
subsystem sequence of paths is estimated separately.
… and is particularly suitable for studies in the early
stages of theory development and testing…”
(Johansson & Yip, 1994, 587)
Min sample size 30 to 100 (Chin and Newstead 1999), but
Green (1991) has a set of ‘rule of thumb’ based on the
number of ‘independent variables’ (predictors) in the model
Combined regression and factor analysis within the
model (measurement model) in each “run”
Suitable for developing constructs and models for
further testing
Negatives of PLS Modelling
Does not allow conventional test for goodness of fit as
per AMOS
Unable to test for co-variance relationships between
variables (constructs)
Not suitable for testing theory (and model)
Irrespective of which technique (AMOS or PLS),
researchers must consider the threat of Common
method bias (Podsakoff et al., 2003)  post-hoc
(Harman’s one factor test) or using method factor (see
Rafferty & Griffin, 2004) or using longitudinal data (see
example in Teo et al., 2013)
T2 Nursing Stress
H7
T2 Effective
Coping Strategies
T1 Role Stress
H4
T1 Admin Stressors
T2 Job Satisfaction
H3
T1 Participation in
change
H1
Time 1
T1 Change
Information
Time 2
Source: Teo et al. (2013) Journal of Nursing Management
Mediation
H10
Multi-Level Analysis
Professor Jarrod Haar
School of Management
Massey University (Albany)
What is Multi-Level?
• Multi-level represents a different level of
analysis
• Single source data is typically analysed with
more ‘simply analysis’ ie regression in
SPSS, to SEM in AMOS or Mplus
• Fundamentally, the relations are on the
same level ie an employee with more worklife balance has more job satisfaction. Thus,
Simple Linear Regression
Why Multi-Level?
• For when we explore relationships that are NOT
on the same level
• This is because data is ‘nested’ e.g., it might be
nested in teams, or follower data is nested under a
specific leader
• This ‘nesting’ effect means ‘Simple Linear
Regression’ is not sufficient – as it does not pick
up and account for these ‘nested’ effects… thus,
multi-level analysis might look like…
Multi-Level Linear Regression
Multi-Level?
• With the advances in technology (statistical
programs) we can now readily conduct multilevel studies where previously these have been
particularly difficult.
• Programs such as MlwiN, Mplus, and HLM
• These approaches are advantageous (from a
theoretical contribution, empirical contribution)
and thus are more readily received in journal
publishing. But, the data is typically harder to get!
Multi-Level Examples
• So, what would these relationships look like?
• Well, in the Pure Sciences it might go from: cell to
organ to person to a family [thus:
cell=neurochemistry; organ=ability to metabolize
ethanol; person=genetic susceptibility to addiction;
family=alcohol abuse in the home]
• In the Social Sciences (Management) we might
explore: employees – teams – departments –
divisions – organisational sites – firms in
industries – etc etc…
Multi-Level Examples
• Specific management examples:
• Teams (collective) IVs (e.g., safety climate) to
individual outcomes (e.g., OCBs, Job Sat, Turnover
Intentions)
• Individuals (e.g., personality styles) to Team
Outcomes (e.g., team performance, team wellbeing)
• Leaders influence (e.g., transformational leadership
style) to Team Outcomes (individual or
collective/team)
Multi-Level Summary
• Fundamentally, the issues and rules behind good
quantitative research simply apply to multi-level
research. But good research needs context specific
analysis – team data in SEM is not right! 
• The data collection might be more onerous (e.g.,
team data is hard – mean majority typically!); and
time demands (e.g., takes longer); issues of trust
(e.g., leaders and their followers)…
• The trade off? You can get away with smaller sample
sizes e.g. Spell et al. (2011) SGR (ABDC=A) had 42
teams (n=174 employees)